Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. - Test2 - elhamkhani - TriplyDB

Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

http://www.wikidata.org/entity/Q35751198

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Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality Mining electronic health records: towards better research applications and clinical care The coming age of data-driven medicine: translational bioinformatics' next frontier TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project Trends in biomedical informatics: automated topic analysis of JAMIA articles Prioritizing Approaches to Engage Community Members and Build Trust in Biobanks: A Survey of Attitudes and Opinions of Adults within Outpatient Practices at the University of Maryland Extracting research-quality phenotypes from electronic health records to support precision medicine Secondary use of clinical data: the Vanderbilt approach Validation and enhancement of a computable medication indication resource (MEDI) using a large practice-based dataset The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future Development and evaluation of an ensemble resource linking medications to their indications Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank Mining the human phenome using semantic web technologies: a case study for Type 2 Diabetes Integrating EMR-linked and in vivo functional genetic data to identify new genotype-phenotype associations The architecture of risk for type 2 diabetes: understanding Asia in the context of global findings.The wave has finally broken: now what?An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithms Applying active learning to high-throughput phenotyping algorithms for electronic health records data Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis.Novel and emerging strategies for longitudinal data collection.Variation in Cohorts Derived from EHR Data in Four Care Delivery Settings.Using EHR-Linked Biobank Data to Study Metformin Pharmacogenomics.Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield Personalized Medicine Research Project Biobank Discriminative and Distinct Phenotyping by Constrained Tensor Factorization.eMERGEing progress in genomics-the first seven years.PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records.Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records.Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus Multiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups.Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems.Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records.Epigenetics in sports.Design patterns for the development of electronic health record-driven phenotype extraction algorithms.The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects

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Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

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Use of diverse electronic medi ...... genome-wide association study.

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Journal of the American Medical Informatics Association

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Use of diverse electronic medi ...... genome-wide association study.

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Aaron W Miller

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Abel N Kho

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Christopher G Chute

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Christopher S Carlson

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Cynthia L Leibson

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David R Crosslin

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Joshua C Denny

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Katherine M Newton

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Laura Rasmussen-Torvik

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Loren L Armstrong

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P2860

Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

PLINK: a tool set for whole-genome association and population-based linkage analyses

A genome-wide association study identifies novel risk loci for type 2 diabetes

Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes

The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies

A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants

Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels

A variant in CDKAL1 influences insulin response and risk of type 2 diabetes

Genetics of type 1 diabetes: what's next?

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212-218

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10.1136/AMIAJNL-2011-000439

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2

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19

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Jennifer A Pacheco

M Geoffrey Hayes

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2011-11-19T00:00:00Z

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51816170

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genome-wide association study

type-2 diabetes

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